AJM 17(4) Web_Master.pdf American Journal of Management Vol. 17(4) 2017 81 CEO Characteristics and the Decision to Include Non-Financial Performance Measures in Compensation Contracts Melloney C. Simerly Western Kentucky University Huiqi Gan School of Business University of Massachusetts Lowell This study examines how CEO characteristics influence the decision to use non-financial performance measures (NFPM) in compensation contracts. Using logistic and OLS regression methods, we examine the CEO characteristics: gender, age, and tenure. We provide limited evidence that female CEOs are positively associated with the use of NFPM and CEO tenure is negatively associated. We also document descriptive information indicating industries that are more likely to use NFPM, and the most common types of NFPM employed. The results of this study further the understanding for the use of NFPM and provide information regarding specific managerial characteristics that influence CEO compensation decisions. INTRODUCTION The use of non-financial performance measures (NFPM) in compensation contracts has been gaining popularity among firms. NFPM include performance indicators such as market share ratios, efficiency and productivity metrics, quality indicators, and innovation measures along with customer and employee satisfaction scores (Ittner, Larcker, & Rajan, 1997). These performance indicators include constructs not incorporated in traditional financial performance measures such as revenue, earnings, or some form of net income (Murphy, 1999; Kaplan & Atkinson, 1998). Kaplan and Atkinson (1998) argue that adopting both financial and non-financial measures for the design of compensation contracts engenders decisions that are based on a long-term perspective, thus decreasing short-term incentives that are not aligned with shareholder interests. The extant literature provides evidence that using NFPM can lead to several benefits that include: better strategic alignment (Kaplan & Norton, 1996; Ittner et al. 1997; Ittner & Larcker, 1998a, 1998b; Banker, Potter, & Srinivasan, 2000; Chenhall, 2003; Ittner, Larcker, & Randall, 2003b), improved performance (Amir & Lev, 1996; Ittner & Larcker, 1998a; Banker et al. 2000; Maines et al. 2002; Said, HassabElnaby, & Wier, 2003; HassabElnaby, Mohammad, & Said, 2010; Van der Stede, Chowand, & Lin, 2006; Hauser, Simester, & Wernerfelt, 1994; Sedatole, 2003), expanded opportunity to assess managerial ability (Kaplan & Norton, 1996; Johnson & Kaplan, 1987; Eccles, 1991), increased robustness in performance measurement (Singleton-Green, 1993; HassabElnaby et al., 2010), and more timely feedback, as well as reduction of risk and noise inherent in financial measures (Lambert & Larcker, 1987; Bruns & McKinnon, 1993; Bushman, Indjejikian, & Smith, 1996; Feltham & 82 American Journal of Management Vol. 17(4) 2017 Xie, 1994; Hemmer, 1996; Davila & Venkatachalam 2004). However, many companies do not use NFPM in the design of chief executive officer (CEO) compensation contracts (Ittner & Larcker, 1998b, 2003). Thus, it is important to understand the factors that lead to the decision to use NFPM. Prior literature highlights the importance of managerial characteristics in firm decisions regarding compensation. Bertrand and Schoar (2003) use panel data to investigate firm level effects resulting from the characteristics of individual managers by using manager mobility across firms. They identify patterns that signal differences in managerial styles and substantiate that managerial fixed effects make a difference in firm level compensation and governance outcomes. Alternatively, by using fixed effects regression methods to separate time invariant effects from the influence of individual managers, Graham, Li, and Qiu (2012) find that manager fixed effects explain a major portion of the variation in levels of executive pay, and they quantify the importance of the influence of managerial characteristics on total executive compensation. This research provides evidence that individual CEOs matter regarding executive pay decisions. In addition, specific CEO traits can affect firm pay structures and corporate governance decisions (Bertrand & Schoar, 2003; Graham et al. 2012). However, this body of literature does not identify what particular CEO characteristics are germane. After reviewing the extant literature pertaining to CEO characteristics in relation to CEO pay structure, we speculate that gender, age, and tenure may influence the decision to use NFPM. The purpose of this study is to provide empirical evidence regarding this supposition. Research using trait theory suggests that the characteristics of leaders and the resultant attributions have repercussions for leadership roles (DeRue, Nahrgang, Wellman, & Humphrey, 2011). In relation to gender, DeRue et al. (2011) contend that attributions made based on perceived differences between men and women can affect leadership outcomes (DeRue et al., 2011). The management literature provides evidence in support of this premise documenting a more negative abnormal stock return after the announcement of a new female CEO compared to the announcement of a new male CEO (Lee & James, 2007). Moreover, prior literature provides evidence that women tend to be more risk averse than men when making financial decisions (Cullis, Jones, & Lewis, 2006; Barber & Odean, 2001; Barua, Davidson, Rama, & Thiruvadi, 2010). Since NFPM can reduce the risk inherent in financial measures (Bruns & Mckinnon, 1993; Feltham & Xie, 1994) we predict that female CEOs will be more positively associated with firms that adopt NFPM. Age and tenure are also consequential to leadership roles and are both influential in the context of compensation structure (Lewellen, Loderer, & Martin, 1987; Finkelstein & Hambrick, 1989; Mehran, 1995; Ryan & Wiggins, 2001). Prior studies regarding CEO age and compensation suggests that both younger and older CEOs have a short-term horizon perspective (Finkelstein & Hambrick, 1989; Ryan & Wiggins, 2001). Younger CEOs are motivated to build their reputation with projects that provide expedient results and older CEOs want to experience the benefits of their labor before they retire. Alternatively, the mixed evidence concerning CEO age and equity compensation suggests that managerial power may impede the optimal contracting environment as CEOs progress through their career (Mehran, 1995; Lewellen et al., 1987; Yermack, 1995). We agree that firms have incentive to include NFPM in CEO contracts for younger and older CEOs due to their short-term horizon perspective. However, we contend that managerial power theory impedes the optimal contracting environment for older and more tenured CEOs (Bebchuk, Fried, & Walker, 2002). This may result in the failure of contracts to include NFPM that engender a long-term horizon perspective. As a consequence, we conclude that CEO age and tenure may be negatively associated with the adoption of NFPM. In addition to hypothesis testing concerning the association of CEO characteristics to the inclusion of NFPM in CEO compensation, we provide data summaries describe the increasing popularity of NFPM and weights applied to NFPM, the industries that are more likely to use NFPM, and the popularity of specific types of NFPM. We test the hypotheses concerning CEO characteristics and the use of NFPM using logistic regression with a dichotomous variable for the inclusion of NFPM as our main dependent variable and ordinary least squares (OLS) regression methods using the weight applied to NFPM as an alternative variable. We offer limited evidence that female CEOs are more likely to opt into compensation American Journal of Management Vol. 17(4) 2017 83 contracts that include NFPM. In addition, we document evidence that CEOs may increasingly adopt a short-term perspective as they age and we find that tenure has a negative relation to NFPM. This study makes several contributions. First, it extends prior literature on NFPM (e.g., Kaplan & Norton, 1996; Ittner et al., 1997; Ittner & Larcker, 1998a, 1998b; Banker et al., 2000; Ittner et al., 2003b; Amir & Lev, 1996; Said et al., 2003; HassabElnaby, Said, & Wier, 2005; Van der Stede et al., 2006; Hauser et al., 1994; Sedatole, 2003) by showing that managerial characteristics and preferences can impact firms� choice of using NFPM. In addition, this research is valuable to those who hire CEOs and to those who design compensation contracts such as the board of directors (BOD) and compensation committee members by demonstrating that CEO power may impede the optimal contracting environment for more tenured CEOs. BODs may want to insist on the inclusion of NFPM to motivate more tenured CEOs to make decisions based on a more forward-looking perspective in order to better align manager and shareholder interests. Furthermore, this investigation assists stakeholders in providing more information about the true nature and focus of a firm. The remainder of this paper is organized as follows. In the next section (section II), we discuss previous literature and develop our hypotheses. We discuss the research design in Section III and empirical results in Section IV. We conclude the paper in Section VI. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Prior literature offers insight on many key managerial traits and characteristics that may influence executive compensation decisions. These characteristics emerge in the extant literature as linked to various risk preferences or differences in managerial horizon perspectives that may be associated with decisions to use NFPM. In our study, we focus on three demographic CEO descriptors: gender, age, and tenure. Gender Byrnes, Miller, and Schafer (1999) conduct a meta-analysis of both self-reported and observed data in the psychology literature and provide evidence that men are less risk-averse than women. Specifically, they find that women are less likely to engage in risky behaviors associated with smoking, using drugs or alcohol, driving, and gambling. Additionally, psychology researchers contribute insight on differences between men and women regarding leadership. In a meta-analysis conducted by DeRue et al. (2011), they conclude that leadership styles differ between men and women, however, gender effects seem to disappear once intelligence and personality differences are considered. Nonetheless, DeRue et al. (2011) contend that the attributions others make about the perceived differences between genders may affect leadership outcomes. Consistent with this premise, the management literature documents a larger negative abnormal stock return after the announcing a new female CEO compared to the announcement of a new male CEO (Lee & James, 2007). The behavioral economics literature offers further insight on decision-making and risk tolerance for men and women. In a computerized laboratory experiment, Powell and Ansic (1997) examine gender differences pertaining to risk preferences and strategic choices in making financial decisions. They manipulate task framing and task familiarity by using an insurance coverage decision (familiar task) and a currency market decision (unfamiliar task). They also vary the amount of money participants can earn as a result of managing costs and the ambiguity associated with the tasks. Powell and Ansic (1997) demonstrate that women are less likely to take risks, irrespective of task framing or the amount of uncertainty associated with the task. This study supports the notion that men and women adopt different strategies for financial decisions. However, these differences do not necessarily affect performance. The behavioral economics literature also offers evidence that men and women adopt different strategies in the financial decision context. Barber and Odean (2001) find that, on average, men trade stock more than women. Although this behavior did not affect performance, they conclude that the increased trading behavior for men may be the result of overconfidence and/or differences in risk tolerance. Additionally, in the accounting literature, Barua et al. (2010) report that CFO gender leads to 84 American Journal of Management Vol. 17(4) 2017 differences in accrual accounting decisions. Their analysis provides evidence that companies with female CFOs have lower performance-matched absolute discretionary accruals and lower absolute accrual estimation errors. Barua et al. (2010) argue that this is likely due to different risk preferences based on gender. This study indicates that not only are women more risk-averse than men, but they are also less likely to engage in earnings manipulation, a consequence of a short-term perspective. NFPM promote a long-term managerial perspective, thereby decreasing short-term actions that are not aligned with shareholder interest (Johnson & Kaplan, 1987; Kaplan & Atkinson, 1998; Singleton- Green, 1993; Kaplan & Norton, 1996, 2001; Bushman et al., 1996; Hemmer, 1996). In addition, NFPM can decrease risk inherent in noisy financial measures and can be a safeguard for managers against circumstances beyond their control (Bruns & Mckinnon, 1993; Feltham & Xie, 1994). Given the evidence that women are more risk-averse than men and exhibit a more long-term perspective when making accounting decisions (Byrnes et al., 1999; Powell & Ansic, 1997; Barber & Odean, 2001; Cullis et al., 2006; Barua et al., 2010), it follows that women may be more likely to be associated with the use of NFPM in compensation contracts. Furthermore, the compensation structure offered may differ depending on the attributions made for female CEOs versus male CEOs (Lee & James, 2007; DeRue et al., 2011). Based on the preceding arguments, we propose the following hypothesis: H1: Female CEOs will be more positively associated with the firms that adopt NFPM for compensation contracts than male CEOs. Age and Tenure Prior literature suggests that CEO age and tenure are also underlying factors in determining CEO remuneration. Finkelstein and Hambrick (1989) investigate the effect of age and tenure on CEO pay levels and find an inverted U-shaped relationship. They explain that this is likely due to changes in the CEO�s personal circumstances. Younger and newer CEOs may have more need for current cash incentives (e.g., mortgage obligations, child rearing expenses, etc.) and this grows as they attain tenure up to a point, then they begin to prefer other types of compensation. Firms also respond to the diverse CEO motivations related to age and tenure. Based on the premise that younger CEOs have an incentive to choose projects with short-term payoffs in order to bolster their reputations and older CEOs have incentive to choose projects that pay off before they retire, Ryan and Wiggins (2001) document that firms pay fewer bonuses to the youngest and oldest managers. They argue that this occurs in order to encourage a long-term decision making for these executives. The results for the relationship between CEO age and equity compensation are mixed. Mehran (1995) finds that older CEOs have less equity pay while, Lewellen et al. (1987) document the opposite. Yermack (1995) specifically tests the relationship between CEO age and the number of stock options awarded. Using agency theory and incorporating horizon problem explanations, he contends that CEOs approaching retirement will avoid investment in long-horizon projects that will only reward their successor. To mitigate this issue, firms increase the amount of performance-based compensation for older CEOs in order to align their interests with firm value maximization. Contrary to theory, Yermack (1995) finds no specific relationship between CEO age and the number of stock options awarded. We contend that this may be due to increasing CEO power. Bebchuk et al. (2002) point out that CEOs often have considerable influence over the appointment of directors and frequently serve on the compensations committee giving them substantial influence over compensation structure decisions and impeding the optimal contracting process. The extant literature reports that CEO tenure is accompanied by competing forces. On one hand, tenure can be an indication of managerial quality. Bushman et al. (1996) document evidence regarding the impact of CEO tenure on performance incentives by examining the relationship between individual performance evaluation and several explanatory variables, including tenure. They find that the importance of individual performance evaluation is positively associated with tenure. Moreover, Davila and Venkatachalam (2004) investigate the role of NFPM in compensation contracts for the airline industry using CEO tenure as a proxy for quality. Their results indicate that passenger load factor (a non-financial American Journal of Management Vol. 17(4) 2017 85 performance measure) is an important determinant for CEO pay and that CEO tenure is associated with higher levels of both cash and total compensation. On the other hand, tenured CEOs can become entrenched and compensation packages may increasingly reflect CEO influence rather than stockholder interests. CEOs can gain control over boards by replacing board members with new directors (Finkelstein & Hambrick, 1989) or by controlling the flow of information to compensation committees (Coughlan & Schmidt, 1985). Hill and Phan (1991) argue that CEO tenure may act as a proxy for the CEO�s ability to exert influence over the BOD in making compensation decisions. They report that both the absolute levels of and changes in CEO cash compensation are decreasingly associated with abnormal stock returns as CEO tenure increases. Considering that prior literature predicts that both CEO age and tenure are associated with a short- term horizon perspective (Finkelstein & Hambrick, 1989; Ryan & Wiggins, 2001) and there are opposing forces (entrenchment and quality) at play concerning CEO tenure (Ryan & Wiggins, 2001). We consider managerial power for the prediction concerning the relation of both age and tenure with the use of NFPM (Bebchuk et al., 2002). We argue that CEO power increases with both age and tenure resulting in compensation structure that reflects a short-term horizon perspective for older and more tenured CEOs. As a result, we offer the following hypothesis concerning CEO tenure and age: H2: CEO age and tenure will be negatively associated with the firms that adopt NFPM for compensation contracts. METHODOLOGY Data and Sample Selection The firms included in the analyses are comprised of 1,017 firms listed on the Standard and Poor�s 500 index (S&P 500) at least once from 1991-2012. The S&P 500 is a valid indicator of firm behavior and performance for the U.S. economy (Fama & French, 2002). We then hand collect the NFPM information by reviewing proxy statement disclosures listed in the U.S. Securities and Exchange Commission Electronic Data-Gathering, Analysis, and Retrieval (EDGAR) database for the years 2000-2014. This results in 9,734 firm-year observations. We then obtain data for the independent variables of interest and control variables using the Excecucomp, Risk Metrics Directors and Compustat databases. Missing data reduce the sample to 5,909 firm-year observations. Empirical Model To test the link between the adoption of NFPM and CEO characteristics, we use the following logistic regression model to test the relation of CEO characteristics to the adoption of NPFM. P(NFPMi,t =1) = 0 + 1CEOGenderi,t + 2CEOAgei,t + 3CEOTenurei,t + 4ROAi,t + 5Leveragei,t + 6Sizei,t + 7Distressi,t + 8Strategyi,t + 9Qualityi,t + 10MktNoisei,t-1 thru t-5 + 11 PercInsBODi,t + 12BODSizei,t +µi + t + i,t, (1) Where: i = observation for each firm; µ = indicator variables for each industry; v = indicator variables for each year; NFPM = binary variable coded as 1 if the firm indicates the use of NFPM in the CEO compensation contract for the year and 0 otherwise; CEOGender = a binary variable coded as 1 for female CEOs and 0 for male CEOs; CEOAge = the age of the CEO in years CEOTenure = the current year minus the year an individual assumed the CEO position; 86 American Journal of Management Vol. 17(4) 2017 ROA = income before extraordinary items divided by lagged total assets; Leverage = ratio of total debt divided by total stockholder equity; Size = natural logarithm of net firm sales; Distress = probability of bankruptcy computed for the previous five years using Ohlson�s (1980) model; Strategy = composite score for organizational strategy using three variables: ratio of research and development to sales, market-to-book ratio, and ratio of number of employees to sales averaged over previous five years; Quality = indicator variable coded as 1 if a firm is a quality award winner listed on Fortune World�s Most Admired list, and 0 otherwise; MktNoise = composite measure using Fisher z-scores for the correlations between return on assets, return on equity, and return on sales, with stock market returns for the five years prior to each proxy date; PercInsBOD = percentage of the board with insider affiliation (employee of the firm or one of the firm affiliates); BODSize = number of directors. Measures Dependent Variable The information for the dependent variable was collected by reviewing proxy statements listed on EDGAR for each firm year. Following Ittner et al. (1997), firms are identified as using NFPM by searching for the keywords: " non-financial," "nonfinancial," "customer satisfaction," "employee satisfaction,� "employee morale," " employee motivation," "quality process," �improvement," " individual objectives," "reengineering," "new product development," diversity," "market share," "productivity," "efficiency," "safety," "innovation," "operational, " "measure," "operational performance," "strategic objectives," "individual performance," and "individual goals." Then, the CEO compensation report was reviewed to ensure that the keyword(s) is used in the appropriate context as a part of CEO remuneration. Firms using both financial and NFPM are coded as one. Firms disclosing only the use of financial performance measures are coded as zero. In addition, the data for the weights placed on NFPM was collected to use as an alternative dependent variable. Independent Variables of Interest The information on CEO gender is available in the Risk Metrics Directors database. Female CEOs are coded as one and male CEOs are coded as zero. CEO age and tenure are obtained from the Execucomp database. The computation for the tenure variable is the difference between the current year and the year the CEO position was assumed. Control Variables We include several firm level controls highighted by prior research to be associated with the use of NFPM. Firm performance, leverage and size are key determinants for the use and retention of NFPM (Said et al., 2003; HassabElnaby et al., 2005). Moreover, financial distress results in a lower likelihood that a firm will adopt NFPM (HassabElnaby et al., 2005). As a result, we include ROA to represent performance, a leverage ratio and net firm sales as a proxy for size. Additionally, we include Ohlson�s (1980) bankruptcy probability measure as an indicator of financial distress. Previous studies offer evidence that firm decisions to retain NFPM are significantly associated with a prospector firm strategy and firms with strong quality initiatives (Ittner et al., 1997; Said et al., 2003; HassabElnaby et al., 2005). To account for these factors we follow Ittner et al. (1997) in computing a composite score to represent firm strategy and include an indicator variable for firms that are listed on Fortune World�s Most Admired list for the sample period. Davila and Venkatachalam (2004) document that the noise in financial performance measures influences the association between NFPM and CEO compensation. Moreover, the use of NFPM are American Journal of Management Vol. 17(4) 2017 87 positively related to the amount of noise inherent in financial measures (Feltham & Xie, 1994; Ittner et al., 1997). Consequently, we follow Ittner et al. (1997) by including a variable to account for market noise by using the firm level correlations between accounting returns and stock market returns (Ittner et al., 1997; Lambert & Larcker, 1987). This measure is constructed by obtaining the Fisher z-score for the correlation between return on assets and stock market returns for the five years prior to each proxy date Core, Holthausen, and Larcker (1999) use CEO compensation as a proxy for assessing board effectiveness because it is observable. Moreover, the BOD has significant power over the level and structure of CEO compensation. Specifically, Core et al. (1999) find that the percentage of inside board members has a negative relation with CEO total compensation, a signal for optimal compensation contracting. Accordingly, the proportion of inside directors may influence whether the BOD approves a CEO compensation package that includes or excludes NFPM. Core et al. (1999) also document that total CEO compensation is positively related to board size. Thus, we include a measure for the percentage of inside board members and board size obtained from the Risk Metrics Directors database. Alternative Dependent Variable Prior research concerning the use of NFPM demonstrates that firms introducing NFPM will need to reduce the weight placed on accounting income for compensation contracts (Hemmer, 1996). This is consistent with predictions made by Kaplan and Atkinson (1998), that firms may come to rely more on long-term indicators of performance (i.e., NFPM) and less on short-term financial measures. Following other studies that investigate the use of NFPM, we also collected information from the firm sample proxy statements concerning the weights applied to NFPM, and then used these as an alternative dependent variable in an analysis examining the relation of the weighted NFPM to CEO characteristics (Ittner et al., 1997; Said et al., 2003; HassabElnaby et al., 2005; HassabElnaby et al., 2010). This analysis is conducted using the following OLS regression model: WeightNFPMi,t = 0 + 1CEOGenderi,t + 2CEOAgei,t + 3CEOTenurei,t + 4ROAi,t + 5Leveragei,t + 6Sizei,t + 7Distressi,t + 8Strategyi,t + 9Qualityi,t + 10MktNoisei,t-1 thru t-5 + 11 PercInsBODi,t + 12BODSizei,t +µi + t + i,t (2) where, WeightNFPM = weight placed on the NPFM if used in CEO compensation contracts. The independent variables for Model (2) are the same as defined for Model (1). RESULTS Descriptives Table 1 contains the 9,734 observations for the full sample collected from the EDGAR database by year (2000-2014), comparing the number of firms that have adopted and the number of firms that have not adopted NFPM for CEO contracting. The graph in figure 1 supports our assertion that the use of NFPM is on the rise, demonstrating that the percentage of firms adopting NFPM has increased since the early 2000s from less than 250 firms in the sample to over 400 firms in 2014. 88 American Journal of Management Vol. 17(4) 2017 TABLE 1 NFPM DISTRIBUTION BY YEAR FIGURE 1 FREQUENCY OF NFPM ADOPTERS BY YEAR Table 2 lists the distribution of the full sample for adopters of NFPM and non-adopters in each industry identified by two-digit SIC code. There are several industries in which all firms in the sample Year Non- adopters Adopters % of NFPM Adopters Total Firms 2000 455 232 33.77% 687 2001 435 251 36.59% 686 2002 436 250 36.44% 686 2003 408 282 40.87% 690 2004 388 293 43.02% 681 2005 358 324 47.51% 682 2006 322 342 51.51% 664 2007 249 402 61.75% 651 2008 215 428 66.56% 643 2009 192 438 69.52% 630 2010 162 460 73.95% 622 2011 152 465 75.36% 617 2012 149 454 75.29% 603 2013 155 447 74.25% 602 2014 159 431 73.05% 590 Total 4,235 5,500 56.50% 9,734 American Journal of Management Vol. 17(4) 2017 89 2-digit SIC code Industry Non- adopters Adopters % of NFPM Adopters Total Firms 1 Agricultural Production 14 100.00% 14 10 Metal Mining 19 31 62.00% 50 12 Coal Mining 13 49 79.03% 62 13 Oil and Gas Extraction 178 260 59.36% 438 14 Mining and Quarrying of Nonmetallic Minerals 7 100.00% 7 15 Building Cnstrctn - General Contractors & Operative Builders 25 44 63.77% 69 16 Heavy Cnstrctn, Except Building Construction - Contractors 23 19 45.24% 42 17 Construction - Special Trade Contractors 11 36.67% 30 20 Food, Beverage 158 221 58.31% 379 21 Tobacco Products 10 41 80.39% 51 22 Textile Mill Products 5 3 37.50% 8 23 Apparel and Other Textile Products 54 34 38.64% 88 24 Lumber and Wood Products 33 36 52.17% 69 25 Furniture and Fixtures 34 11 24.44% 45 26 Paper and Allied Products 47 80 62.99% 127 27 Printing and Publishing 51 49 49.00% 100 28 Chemicals and Allied Products 274 422 60.63% 696 29 Petroleum 14 70 83.33% 84 30 Rubber 45 35 43.75% 80 31 Leather and Leather Products 17 20 54.05% 37 32 Stone, Clay, & Glass Products 24 14 36.84% 38 33 Primary Metal Industries 62 58 48.33% 120 34 Fabricated Metal Products 61 32 34.41% 93 35 Industrial Machinery and Computer Equipment 262 390 59.82% 652 36 Electronic and Other Electric Equipment 249 373 59.97% 622 37 Transportation Equipment 91 184 66.91% 275 38 Instruments and Related Products 144 280 66.04% 424 39 Miscellaneous Manufacturing 38 7 15.56% 45 40 Railroad Transportation 30 40 57.14% 70 41 Local, Suburban Transit & Interurbn Hgwy Passenger Transpo 3 1 25.00% 4 42 Motor Freight Transportation 26 19 42.22% 45 44 Water Transportation 8 7 46.67% 15 45 Transportation by Air 7 60 89.55% 67 47 Transportation Services 8 17.78% 45 48 Communication 134 184 57.86% 318 49 Electric, Gas and Sanitary Services 180 507 73.80% 687 NFPM have adopted NFPM. These include Agricultural Production (SIC code 1) and Mining and Quarrying of Nonmetallic Minerals (SIC code 14). However, the number of firms in our sample representing these industries is small. Among industries with more than 500 firms represented, Electric, Gas and Sanitary Services (SIC code 49) is the industry in which the adoption of NFPM is most popular with almost 74% of firms adopting NFPM followed by Chemicals and Allied Products (SIC code 28) where almost 61% of firms in the sample have adopted NFPM. TABLE 2 SAMPLE DISTRIBUTION FOR THE USE OF NFPM BY INDUSTRY 90 American Journal of Management Vol. 17(4) 2017 2-digit SIC code Industry Non- adopters Adopters % of NFPM Adopters Total Firms 50 Wholesale�Durable Goods 50 26 34.21% 76 51 Wholesale�Non-Durable Goods 30 47 61.04% 77 52 Building Matrials, Hrdwr, Garden Supply & Mobile Home De 17 27 61.36% 44 53 General Merchandise Store 90 86 48.86% 176 54 Food Stores 20 22.47% 89 55 Automotive Dealers and Gasoline Service Stations 38 34 47.22% 72 56 Apparel and Accessory Stores 105 42 28.57% 147 57 Home Furniture, Furnishings and Equipment Stores 44 24 35.29% 68 58 Eating and Drinking 56 43 43.43% 99 59 Miscellaneous Retail 106 47 30.72% 153 60 Depository Institutions 220 249 53.09% 469 61 Nondepository Credit Institutions 59 60.82% 97 62 Security & Commodity Brokers, Dealers, Exchanges & Service 102 132 56.41% 234 63 Insurance Carriers 217 208 48.94% 425 64 Insurance Agents, Brokers and Service 15 15 50.00% 30 65 Real Estate 2 18 90.00% 20 67 Holding and Other Investment Offices 140 135 49.09% 275 70 Hotels, Rooming Houses, Camps, and Other Lodging Places 9 29 76.32% 38 72 Personal Services 25 9 26.47% 34 73 Business Services 343 451 56.80% 794 75 Automotive Repair, Services and Parking 18 12 40.00% 30 78 Motion Pictures 4 9 69.23% 13 79 Amusement and Recreation Services 24 14 36.84% 38 80 Health Services 41 73 64.04% 114 82 Educational Services 16 23 58.97% 39 87 Engineering and Management Services 12 30 71.43% 42 99 Nonclassifiable Establishments 29 16 35.56% 45 Total 4,235 5,500 56.50% 9,734 TABLE 2 (continued) SAMPLE DISTRIBUTION FOR THE USE OF NFPM BY INDUSTRY Table 3 tabulates the distribution for the number of NFPM adopted for each firm observation collected from EDGAR. Most firms adopt one or two NFPM for CEO compensation. American Journal of Management Vol. 17(4) 2017 91 TABLE 3 YEARLY DISTRIBUTION FOR THE NUMBER OF NFPM ADOPTED Table 4 contains the distribution for the types of NFPM adopted for the full sample of 9,734 firm year observations. The most popular NFPM is strategic objectives, consistent with numerous studies highlighting strategic alignment as a benefit of using NFPM (Kaplan & Norton, 1996; Ittner et al., 1997; Ittner & Larcker, 1998a, 1998b; Banker et al., 2000; Chenhall, 2003; Ittner et al,. 2003b). The second most popular type of NFPM is operational performance, followed by safety and customer satisfaction. Figure 2 is a graphical display of this information. Year 0 1 2 3 4 Total 2000 454 145 48 26 14 687 2001 435 157 52 26 16 686 2002 436 151 49 29 21 686 2003 408 165 51 36 30 690 2004 388 162 63 42 26 681 2005 357 165 82 48 30 682 2006 320 173 87 54 30 664 2007 248 172 127 56 48 651 2008 215 185 110 78 55 643 2009 190 180 107 84 69 630 2010 161 184 121 94 62 622 2011 152 161 144 91 69 617 2012 149 150 137 95 72 603 2013 155 149 133 101 64 602 2014 159 166 131 78 56 590 Total 4,227 2,465 1,442 938 662 9,734 Number of NFPM Adopted 92 American Journal of Management Vol. 17(4) 2017 TABLE 4 TYPES OF NFPM ADOPTED BY FIRMS NFPM Type Number of Firms Customer satisfaction 1070 Employee satisfaction 219 Quality process 108 Re-engineering or reengineering 3 New product development 136 Diversity 754 Market share 952 Productivity 574 Efficiency 618 Safety 1169 Innovation 713 Operational measure 113 Operational performance 1352 Strategic objectives 2029 Nonfinancial goals (unspecified) 988 American Journal of Management Vol. 17(4) 2017 93 FIGURE 2 TYPES OF NFPM ADOPTED BY FIRMS Table 5 describes that data collected for our alternative dependent variable, the weight placed on NFPM. The percentage of the weight applied to NFPM is tabulated by year. Given the descriptive results presented in Table 1 and Table 5, the data collected demonstrate not only an increase in the adoption of NFPM among firms listed on the S&P 500 but also an increase in the relative weight applied to these measures. 94 American Journal of Management Vol. 17(4) 2017 TABLE 5 NFPM WEIGHT DISTRIBUION BY YEAR Hypotheses Testing We begin our analysis of the relation between NFPM and CEO characteristics by examining the descriptive statistics for the available sample after matching the data collected from proxy statements (9,734 observations) to the Excecucomp, Risk Metrics Directors and Compustat databases leading to a sample of 5,909 firm-year observations for our regression analyses. Table 6 Panel A contains the descriptive statistics. The mean for NFPM is 0.609; consequently, over half of the firm year observations report the use of both financial and NFPM for CEO remuneration. With respect to the independent variables of interest, 2.2 percent of the sample are female CEOs. The median for age is 56 years and average CEO tenure is slightly more than 7 years. Panel B of Table 6 contains the difference tests and descriptive statistics for the variables of interest and the control variables comparing firms that have adopted NFPM with firms that do not include NFPM in CEO compensation structure. According to the univariate tests, more women are associated with firms that use NFPM (p < 0.001). The two groups of firms have CEOs with relatively the same age, however, firms that use NFPM employ CEOs that have significantly less tenure. Panels C, D and E of Table 6 contain the frequency of using NFPM by gender, by the median of age, and by the median of tenure. We also test the mean difference and median difference using t-tests and Wilcoxon tests. These analyses show that the female group is significantly more likely to be associated with adopters of NFPM and the weights applied to NFPM compared to the male group. However, there are no significant differences for age for both the mean and median groups that are either above or below the mean/median. The difference in the frequency of using NFPM between the tenure below median group and the tenure above median group for the frequency of NFPM adoption and the weights assigned to NFPM for CEO compensation is significant. According to these comparisons, more tenured CEOs are less likely to opt into contracts that include NFPM. Year Non- adopters Adopters % for Weight NFPM Adopters Total Firms 2000 667 20 2.91% 687 2001 662 24 3.50% 686 2002 665 21 3.06% 686 2003 663 27 3.91% 690 2004 646 35 5.14% 681 2005 634 48 7.04% 682 2006 607 57 8.58% 664 2007 558 93 14.29% 651 2008 539 104 16.17% 643 2009 517 113 17.94% 630 2010 507 115 18.49% 622 2011 502 115 18.64% 617 2012 486 117 19.40% 603 2013 484 118 19.60% 602 2014 500 90 15.25% 590 Total 8,637 1,097 11.27% 9,734 American Journal of Management Vol. 17(4) 2017 95 Panel F of Table 6 contains the correlations for the dependent variable (NFPM), the variables representing CEO characteristics, and the control variables. As expected, NFPM is significantly and positively related to CEOgender, indicating the women are more risk-averse than men (Byrnes et al., 1999; Powell & Ansic, 1997; Barber & Odean, 2001; Cullis et al., 2006; Barua et al., 2010). Age is not significantly correlated with NFPM. Alternatively, NFPM is negatively correlated with tenure, indicating that as CEOs gain tenure they may begin to take on a short-term perspective. TABLE 6 PANEL A: DESCRIPTIVE STATISTICS FOR REGRESSION SAMPLE NFPM = binary variable coded as 1 if the firm indicates the use of NFPM in the CEO compensation contract for the year and 0 otherwise; WeightNFPM = weight placed on the NPFM if used in CEO compensation contracts; CEOGender = a binary variable coded as 1 for female CEOs and 0 for male CEOs; CEOAge = the age of the CEO in years CEOTenure = the current year minus the year an individual assumed the CEO position; ROA = income before extraordinary items divided by lagged total assets; Leverage = ratio of total debt divided by total stockholder equity; Size = natural logarithm of net firm sales; Distress = probability of bankruptcy computed for the previous five years using Ohlson�s (1980) model; Strategy = composite score for organizational strategy using three variables: ratio of research and development to sales, market-to-book ratio, and ratio of number of employees to sales averaged over previous five years; Quality = indicator variable coded as 1 if a firm is a quality award winner listed on Fortune World�s Most Admired list, and 0 otherwise; MktNoise = composite measure using Fisher z-scores for the correlations between return on assets, return on equity, and return on sales, with stock market returns for the five years prior to each proxy date; Variable N Mean Std. Dev. 25th Percentile Median 75th Percentile Min Max NFPM 5909 0.609 0.488 0.000 1.000 1.000 0.000 1.000 NFPMWeight 5909 0.038 0.113 0.000 0.000 0.000 0.000 1.000 Gender 5909 0.022 0.148 0.000 0.000 0.000 0.000 1.000 Age 5909 55.847 6.620 51.000 56.000 60.000 34.000 82.000 Tenure 5909 7.060 6.094 3.000 5.000 9.000 1.000 51.000 ROA 5909 0.070 0.095 0.031 0.066 0.110 -1.747 0.610 Leverage 5909 0.570 0.199 0.443 0.576 0.697 0.032 1.800 Size 5909 8.856 1.261 7.934 8.784 9.754 4.873 12.757 Distress 5909 0.165 0.146 0.049 0.125 0.241 0.000 0.983 Strategy 5909 0.004 0.169 -0.081 -0.040 0.032 -0.140 6.164 Quality 5909 0.189 0.392 0.000 0.000 0.000 0.000 1.000 MktNoise 5909 -0.042 0.923 -0.636 -0.049 0.529 -3.793 4.575 PercInsBOD 5909 0.754 0.148 0.667 0.786 0.875 0.000 1.000 BODSize 5909 10.246 2.393 9.000 10.000 12.000 4.000 34.000 96 American Journal of Management Vol. 17(4) 2017 PercInsBOD = percentage of the board with insider affiliation (employee of the firm or one of the firm affiliates); BODSize = number of directors. TABLE 6 (continued) PANEL B: DESCRIPTIVE STATISTICS FOR NFPM ADOPTERS (NON-ADOPTERS) N Mean Median N Mean Median t-test Wilcoxon Gender 2312 0.013 0.000 3597 0.028 0.000 0.000 0.000 Age 2312 55.812 56.000 3597 55.870 56.000 0.745 0.451 Tenure 2312 7.850 6.000 3597 6.552 5.000 0.000 0.000 ROA 2312 0.065 0.066 3597 0.074 0.066 0.001 0.371 Leverage 2312 0.548 0.558 3597 0.583 0.589 0.000 0.000 Size 2312 8.408 8.354 3597 9.144 9.105 0.000 0.000 Distress 2312 0.167 0.126 3597 0.164 0.124 0.439 0.104 Strategy 2312 0.024 -0.033 3597 -0.008 -0.046 0.000 0.000 Quality 2312 0.151 0.000 3597 0.214 0.000 0.000 0.000 MktNoise 2312 -0.017 0.006 3597 -0.058 -0.080 0.090 0.020 PercInsBOD 2312 0.711 0.750 3597 0.782 0.818 0.000 0.000 BODSize 2312 9.881 10.000 3597 10.480 10.000 0.000 0.000 The variable definitions are the same as those defined in Panel A. Adopters Difference TestsNon-adopters American Journal of Management Vol. 17(4) 2017 97 TABLE 6 (continued) TABLE 6 (continued) PANEL F: CORRELATION MATRIX FOR DEPENDENT VARIABLES, INDEPENDENT VARIABLES OF INTEREST AND CONTROL VARIABLES Tests of H1 and H2 Hypothesis 1 states that female CEOs will be more positively associated with the use of NFPM in compensation contracts than male CEOs while hypothesis 2 predicts that both age and tenure will be negatively associated with the adoption of NFPM. Using model 1 and including gender, age and tenure as independent variables of interest, the estimated coefficient for gender is not compelling. Thus, hypothesis 1 is not supported. Additionally, the coefficient for age is also not significant. However, the coefficient for tenure is negative and significant (p < 0.01). Therefore, more tenured CEOs are less likely to opt into compensation contracts that include NFPM and firms are less likely to include these measures for after controlling for gender and age. Table 7 contains the results. Difference Tests Mean Median Mean Median t-test Wilcoxon Frequencies of NFPM Use 0.605 1.000 0.773 1.000 0.000 0.000 WeightNFPM 0.037 0.000 0.058 0.000 0.038 0.000 Difference Tests Mean Median Mean Median t-test Wilcoxon Frequencies of NFPM Use 0.604 1.000 0.613 1.000 0.453 0.453 WeightNFPM 0.038 0.000 0.038 0.000 0.919 0.951 Panel E: Frequencies of NFPM use by Below v.s. Above Median Tenure Mean Median Mean Median t-test Wilcoxon Frequencies of NFPM Use 0.651 1.000 0.576 1.000 0.000 0.000 WeightNFPM 0.044 0.000 0.033 0.000 0.000 0.000 Panel C: Frequencies of NFPM use by Gender Panel D: Frequencies of NFPM use by Below v.s. Above Median Age Low High Male Female Low High Difference Tests NFPM 1 WeightNFPM 0.2681* 1 Gender 0.0508* 0.0270* 1 Age 0.0042 0.0001 -0.0636* 1 Tenure -0.1039* -0.0490* -0.0596* 0.3839* 1 ROA 0.0438* -0.0243* 0.0005 0.0596* 0.0802* 1 Leverage 0.0851* 0.0231* 0.0890* 0.0392* -0.1304* -0.1790* 1 Size 0.2849* 0.1291* 0.0526* 0.1214* -0.0598* -0.0914* 0.2492* 1 Distress -0.0101 -0.0166 0.0672* -0.0144 -0.1141* -0.2176* 0.7792* -0.0806* 1 Strategy -0.0911* -0.0601* 0.0132 -0.1206* 0.0558* 0.1860* -0.2837* -0.2459* -0.1782* 1 Quality 0.0777* 0.0441* 0.0614* 0.0627* -0.0199 0.0229* 0.0165 0.3678* -0.1061* -0.0400* 1 MktNoise -0.0221* -0.0096 0.0086 -0.0391* -0.0273* -0.1209* -0.0088 -0.0445* -0.0199 0.0277* 0.0063 1 PercInsBOD 0.2329* 0.1386* 0.0571* 0.0345* -0.0646* -0.0388* 0.2025* 0.2741* 0.0954* -0.1818* 0.0844* -0.0291* 1 BODSize 0.1222* 0.0511* 0.0199 0.1003* -0.0995* -0.0638* 0.2284* 0.4802* 0.0539* -0.2203* 0.1845* -0.0196 0.1134* 1 *, **, *** indicates significance at the .10, .05, .01 levels respectively. The variable definitions are the same as those defined in Panel A. 98 American Journal of Management Vol. 17(4) 2017 TABLE 7 LOGISTIC REGRESSION ANALYSIS FOR THE RELATION OF CEO GENDER, AGE AND TENURE TO THE USE OF NFPM IN CEO COMPENSATION Prob(NFPM) WeightNFPM Coeff. Coeff. (z-stat) (t-stat) Gender 0.348 -0.001 (1.38) (-0.09) Age -0.001 0.000 (-0.14) (0.46) Tenure -0.038*** -0.001*** (-5.87) (-3.54) ROA 2.038*** -0.018 (5.32) (-1.45) Leverage -0.162 -0.025* (-0.54) (-1.92) Size 0.338*** 0.003* (8.52) (1.73) Distress 0.739* 0.010 (1.83) (0.55) Strategy 0.034 -0.016*** (0.20) (-2.72) Quality 0.061 0.014*** (0.66) (2.98) MktNoise 0.019 0.000 (0.54) (0.12) PercInsBOD 0.708*** 0.024** (2.79) (2.49) BODSIze 0.044*** -0.000 -2.6 (-0.14) Constant -4.539*** -0.074*** (-7.71) (-3.69) Industry indicators included Yes Yes Year indicators included Yes Yes N 5,813 5,909 Pseudo R2/R2 0.18 0.092 *, **, *** indicates significance at the .10, .05, .01 levels respectively. The variable definitions are the same as defined in Table 6 Panel A. Taking the log of tenure and age produces similar results. American Journal of Management Vol. 17(4) 2017 99 The analysis retains 5,813 firm year observations. The proxy for performance (ROA) and Size are both positively associated with NFPM (Core et al., 1999; Said et al., 2003). Contrary to prior literature Distress is positively associated with the use of NFPM (Said et al., 2003). Although the coefficient is only marginally significant, this may indicate that firms are beginning to use these beneficial measures to improve their future performance. Additionally, PercInsBOD and BODSize is positively and significantly associated with the use of NFPM (p < 0.01), suggesting that greater BOD independence and larger boards seek to offer the most optimal compensation contracting. The results for the independent variables of interest using model (2) employing the alternative dependent variable NFPMWeight are consistent with model (1). Tenure is negative and significantly associated with weights applied to NFPM, while gender and age are not. Regarding the control variables, the results are consistent with expectations with the exception of the proxy for strategy. Our analysis implies that firms with a prospector strategy and less likely to apply weights to NFPM. The results are tabulated in table 7. Given the small number of observations that are female and the correlation between age and tenure, we then analyze the independent variables of interest individually in separate models. Consistent with hypothesis 1, female CEOs are positively associated with the use of NFPM in compensation contracts. Alternatively, age and tenure are both negatively associated with the adoption of NFPM for compensation contracting. We conduct a similar analysis using the alternative dependent variable, NFPMWeight and find that tenure is negatively associated with the adoption of NFPM while outcomes for gender and age are inconclusive. The results are contained in table 8. 100 American Journal of Management Vol. 17(4) 2017 TABLE 8 LOGISTIC REGRESSION ANALYSIS FOR THE RELATION OF CEO GENDER, AGE, AND TENURE TO THE USE OF NFPM IN CEO COMPENSATION ANALYZED INDIVIDUALLY USING SEPARATE MODELS Prob(NFPM) NFMP_WEIGHT Prob(NFPM) NFMP_WEIGHT Prob(NFPM) NFMP_WEIGHT Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. (z-stat) (t-stat) (z-stat) (t-stat) (z-stat) (t-stat) Gender 0.428* 0.000 (1.73) (0.04) Age -0.016*** -0.000 (-3.28) (-1.01) Tenure -0.038*** -0.001*** (-6.53) (-4.02) ROA 2.032*** -0.018 1.947*** -0.020 1.869*** -0.021* (5.30) (-1.42) (5.14) (-1.54) (4.91) (-1.65) Leverage -0.158 -0.025* -0.110 -0.024* -0.102 -0.024* (-0.53) (-1.91) (-0.38) (-1.81) (-0.35) (-1.81) Size 0.339*** 0.003* 0.340*** 0.003* 0.333*** 0.003* (8.54) (1.75) (8.53) (1.77) (8.42) (1.74) Distress 0.761* 0.010 0.811** 0.010 0.775* 0.010 (1.89) (0.54) (2.04) (0.59) (1.95) (0.60) Strategy 0.046 -0.016*** -0.026 -0.018*** 0.009 -0.017*** (0.27) (-2.73) (-0.16) (-2.83) (0.05) (-2.81) Quality 0.072 0.014*** 0.082 0.014*** 0.068 0.014*** (0.78) (2.98) (0.91) (3.01) (0.75) (2.99) MktNoise 0.019 0.000 0.020 0.000 0.021 0.000 (0.54) (0.11) (0.56) (0.13) (0.60) (0.13) PercInsBOD 0.717*** 0.024** 0.759*** 0.025*** 0.750*** 0.025*** (2.82) (2.49) (3.01) (2.62) (2.99) (2.63) BODSize 0.043** -0.000 0.053*** 0.000 0.052*** 0.000 (2.57) (-0.11) (3.15) (0.17) (3.07) (0.14) Constant -4.608*** -0.069*** -3.961*** -0.066*** -4.729*** -0.076*** (-8.67) (-4.03) (-6.89) (-3.37) (-8.98) (-4.41) Industry indicators included Yes Yes Yes Yes Yes Yes Year indicators included Yes Yes Yes Yes Yes Yes N 5,813 5,909 5,813 5,909 5,813 5,909 Pseudo R2/R2 0.180 0.092 0.174 0.090 0.173 0.090 *, **, *** indicates significance at the .10, .05, .01 levels respectively. The variable definitions are the same as defined in Table 6 Panel A. American Journal of Management Vol. 17(4) 2017 101 CONCLUSIONS This study provides empirical evidence regarding whether particular CEO characteristics lead to a greater likelihood of using NFPM. Specifically, we provide limited evidence that female CEOs are positively associated with the use of both financial and NFPM in CEO remuneration. Given that NFPM provide a tool for mitigating risk inherent in using only financial performance measures (Bruns & McKinnon, 1993; Feltham & Xie, 1994), this result is consistent with prior literature suggesting that women are more risk-averse than men (Byrnes et al., 1999; Powell & Ansic, 1997; Barber & Odean, 2001). There are two distinct elements to consider regarding gender and executive compensation. First, the CEO must accept or opt into an agreed-upon contract with the types of performance measures specified. Secondly, those in authority over the structure of compensation contracting (i.e. BOD, compensation committee) include certain types of performance measures. The particular performance measures included could be the consequence of attributions made to the executive based on their gender (Lee & James, 2007; DeRue et al., 2011). The results presented by this study complement the evidence provided by Barua et al. (2010) that female CEOs make decisions based on a more long-term perspective than their male counterparts. We postulate that our results concerning gender are weak due to the small number of female CEOs in the sample. The results for CEO age and tenure support the existence of an entrenchment issue (Ryan & Wiggins, 2001) and an increasingly short-term horizon perspective (Finkelstein & Hambrick, 1989) as CEOs get older and gain tenure. When controlling for gender and tenure, CEO age has no relation to the adoption of NFPM for compensation contracting. However, when gender and tenure are not considered, age is negatively associated with the use of NFPM suggesting that as CEOs get older they may begin to have a short-term horizon perspective (Yermack, 1995). Consistent throughout our analyses, CEO tenure is negatively and significantly associated with the use of NFPM. This suggests that CEO power may increase with tenure. Although the board should include NFPM to combat the CEO�s increasingly short- term perspective, CEOs may use their influence to structure compensation contracts that fail to engender a long-term perspective because they prefer to avoid measures that may only reward their successors. Many studies show that the fixed effects of managers matter in firm level compensation and governance outcomes (Bertrand & Schoar, 2003). Further, prior research has documented that several firm characteristics including strategic orientation, industry norms, and performance effects are associated with the use of NFPM (Ittner et al., 1997; Said et al., 2003; HassabElnaby et al., 2005). However, previous research does not address what particular CEO characteristics lead to the adoption of NFPM. The evidence presented in this study demonstrates that gender, age, and tenure are affiliated with the use of NFPM in CEO remuneration. This research is valuable to those who hire CEOs and to those who design compensation contracts (i.e., boards of directors and compensation committee members). Moreover, given that controls for corporate governance were considered, the results of this study suggests that executives may play a larger role in the compensation package compromise (between the CEO and the BOD) than do firm directors. The contributions are also informative to investors who want to ensure they are providing support to firms with a leader whose focus is aligned with their investment strategy. Finally, this investigation may assist stakeholders by contributing additional information about the true nature and focus of a firm, based on the characteristics of the CEO. 102 American Journal of Management Vol. 17(4) 2017 ACKNOWLEDGEMENT This paper is a collaboration born out of Melloney Simerly's dissertation at Virginia Commonwealth University. We would like to thank the committee members who provided valuable guidance throughout her time there: Dr. Benson Wier (Chair), Dr. Myung Park, Dr. Leslie Stratton and Dr. Jean Zhang. We would also like to express our appreciation to Thomas Lewis, Taylor Bennett, Lewis Rogers and Rachel Hanks for assisting in the data collection process. 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